AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations

Bowler, Ellen ORCID: https://orcid.org/0000-0002-9681-1355; Byrne, James ORCID: https://orcid.org/0000-0003-3731-2377; Leclerc, Lisa-Marie; Roberto-Charron, Amélie; Rogers, Martin S.J. ORCID: https://orcid.org/0000-0003-0056-2030; Cavanagh, Rachel D. ORCID: https://orcid.org/0000-0002-2474-9716; Harasimo, Jason; Lancaster, Melanie L.; Chan, Ryan S.Y.; Strickson, Oliver; Wilkinson, Jeremy ORCID: https://orcid.org/0000-0002-7166-3042; Downie, Rod; Hosking, J. Scott ORCID: https://orcid.org/0000-0002-3646-3504; Andersson, Tom R. ORCID: https://orcid.org/0000-0002-1556-9932. 2025 AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations. Ecological Solutions and Evidence, 6 (2), e70034. 15, pp. 10.1002/2688-8319.70034

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Text (Open Access) © 2025 The Author(s). Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society. Ecol Sol and Evidence - 2025 - Bowler - AI sea ice forecasts for Arctic conservation A case study predicting the timing of.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (4MB) | Preview

Abstract/Summary

Every autumn on the south coast of Victoria Island (Nunavut, Canada), endangered Dolphin and Union (DU) caribou (Rangifer tarandus groenlandicus x pearyi) wait for sea ice to form before continuing their southwards migration to the mainland. Delayed freeze-up, less stable ice conditions and ice-breaking by vessels are putting migrating caribou at risk, but unpredictable freeze-up times pose challenges for conservation planning. Having early warning of when the caribou sea ice crossing is likely to take place could guide more targeted measures (e.g., ice-breaking vessel management). In this case study, we use a multi-stakeholder approach to explore the potential of using observed and forecast sea ice concentration (SIC) to predict when DU caribou are likely to cross the sea ice. We examine links between caribou movement records and coincident satellite observations of SIC collected between 1996–2005 and 2015–2019. We establish probabilistic “percent-crossed” metrics to convert SIC freeze-up profiles into anticipated sea ice crossing-start date ranges and maps. Finally, we assess the potential of using IceNet, an AI-based 25 km resolution SIC forecast model, to predict these crossing-start ranges in 2020–2022. We identify a clear link between SIC freeze-up profiles and crossing-start times, with median SIC reaching 98.8% (IQR = 94.1%, 100%) when caribou start their crossings. Our percent-crossed metrics are effective in converting SIC records into crossing-start date maps which can guide human experts. IceNet results show promise, predicting crossing-start ranges comparable to those observed in 2022 up to three weeks before the first observed sea ice crossing. In 2021, IceNet's predicted ranges are systematically early, but improve between three- to one-week lead times. Practical implication: AI sea ice forecasts could provide early warning of DU caribou sea ice crossing times, informing mitigation of ice-breaking vessels and providing a blueprint applicable to other ice-dependent species. Our case study contributes practical considerations, limitations and areas for future research to drive innovation in this emerging field forward. Ultimately, forecasts could be integrated into human-expert centred decision-support tools, guiding dynamic conservation and management for Arctic species.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1002/2688-8319.70034
ISSN:	2688-8319
Additional Keywords:	Dolphin and Union caribou, migration, sea ice forecast, Artificial Intelligence (AI), dynamic conservation, GPS tracking, Rangifer, Arctic Northwest Passage
Date made live:	29 May 2025 13:40 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539350

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